Overview

Dataset statistics

Number of variables36
Number of observations10000
Missing cells240372
Missing cells (%)66.8%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory2.7 MiB
Average record size in memory288.0 B

Variable types

Numeric10
Categorical11
Unsupported15

Alerts

UPN has a high cardinality: 8409 distinct valuesHigh cardinality
EntryDate has a high cardinality: 417 distinct valuesHigh cardinality
TermlySessionsPossible is highly overall correlated with / and 1 other fieldsHigh correlation
/ is highly overall correlated with TermlySessionsPossible and 1 other fieldsHigh correlation
\ is highly overall correlated with TermlySessionsPossible and 1 other fieldsHigh correlation
S is highly overall correlated with P and 2 other fieldsHigh correlation
P is highly overall correlated with S and 1 other fieldsHigh correlation
V is highly overall correlated with UHigh correlation
R is highly overall correlated with SHigh correlation
U is highly overall correlated with P and 1 other fieldsHigh correlation
Y is highly overall correlated with SHigh correlation
EnrolStatus is highly imbalanced (79.2%)Imbalance
Surname has 10000 (100.0%) missing valuesMissing
Forename has 10000 (100.0%) missing valuesMissing
Middlenames has 10000 (100.0%) missing valuesMissing
PreferredSurname has 10000 (100.0%) missing valuesMissing
FormerSurname has 10000 (100.0%) missing valuesMissing
DoB has 10000 (100.0%) missing valuesMissing
B has 10000 (100.0%) missing valuesMissing
J has 10000 (100.0%) missing valuesMissing
L has 5599 (56.0%) missing valuesMissing
P has 9331 (93.3%) missing valuesMissing
V has 7227 (72.3%) missing valuesMissing
W has 10000 (100.0%) missing valuesMissing
C has 5884 (58.8%) missing valuesMissing
E has 10000 (100.0%) missing valuesMissing
H has 10000 (100.0%) missing valuesMissing
I has 4369 (43.7%) missing valuesMissing
M has 6954 (69.5%) missing valuesMissing
R has 8544 (85.4%) missing valuesMissing
S has 9897 (99.0%) missing valuesMissing
T has 10000 (100.0%) missing valuesMissing
G has 10000 (100.0%) missing valuesMissing
N has 10000 (100.0%) missing valuesMissing
O has 7726 (77.3%) missing valuesMissing
U has 9913 (99.1%) missing valuesMissing
D has 10000 (100.0%) missing valuesMissing
X has 5423 (54.2%) missing valuesMissing
Y has 9505 (95.0%) missing valuesMissing
UPN is uniformly distributedUniform
Surname is an unsupported type, check if it needs cleaning or further analysisUnsupported
Forename is an unsupported type, check if it needs cleaning or further analysisUnsupported
Middlenames is an unsupported type, check if it needs cleaning or further analysisUnsupported
PreferredSurname is an unsupported type, check if it needs cleaning or further analysisUnsupported
FormerSurname is an unsupported type, check if it needs cleaning or further analysisUnsupported
DoB is an unsupported type, check if it needs cleaning or further analysisUnsupported
B is an unsupported type, check if it needs cleaning or further analysisUnsupported
J is an unsupported type, check if it needs cleaning or further analysisUnsupported
W is an unsupported type, check if it needs cleaning or further analysisUnsupported
E is an unsupported type, check if it needs cleaning or further analysisUnsupported
H is an unsupported type, check if it needs cleaning or further analysisUnsupported
T is an unsupported type, check if it needs cleaning or further analysisUnsupported
G is an unsupported type, check if it needs cleaning or further analysisUnsupported
N is an unsupported type, check if it needs cleaning or further analysisUnsupported
D is an unsupported type, check if it needs cleaning or further analysisUnsupported

Reproduction

Analysis started2023-06-26 14:08:12.285094
Analysis finished2023-06-26 14:08:38.433989
Duration26.15 seconds
Software versionpandas-profiling v3.6.6
Download configurationconfig.json

Variables

Estab
Real number (ℝ)

Distinct9624
Distinct (%)96.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean189996.86
Minimum47661
Maximum294634
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size78.2 KiB
2023-06-26T15:08:38.681527image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum47661
5-th percentile131037.6
Q1165887.25
median189538.5
Q3214632.25
95-th percentile250297.65
Maximum294634
Range246973
Interquartile range (IQR)48745

Descriptive statistics

Standard deviation35936.561
Coefficient of variation (CV)0.18914292
Kurtosis-0.13092719
Mean189996.86
Median Absolute Deviation (MAD)24441
Skewness-0.026891835
Sum1.8999686 × 109
Variance1.2914364 × 109
MonotonicityNot monotonic
2023-06-26T15:08:38.972764image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
179546 3
 
< 0.1%
204610 3
 
< 0.1%
174493 3
 
< 0.1%
207899 3
 
< 0.1%
205156 3
 
< 0.1%
222696 3
 
< 0.1%
175277 3
 
< 0.1%
188902 3
 
< 0.1%
157709 3
 
< 0.1%
208773 3
 
< 0.1%
Other values (9614) 9970
99.7%
ValueCountFrequency (%)
47661 1
< 0.1%
49870 1
< 0.1%
63686 1
< 0.1%
63702 1
< 0.1%
74526 1
< 0.1%
74597 1
< 0.1%
75306 1
< 0.1%
77498 1
< 0.1%
78077 1
< 0.1%
78759 1
< 0.1%
ValueCountFrequency (%)
294634 1
< 0.1%
294250 1
< 0.1%
294207 1
< 0.1%
294178 1
< 0.1%
294081 1
< 0.1%
293246 1
< 0.1%
292276 1
< 0.1%
291596 1
< 0.1%
290638 1
< 0.1%
290463 1
< 0.1%

UPN
Categorical

HIGH CARDINALITY  UNIFORM 

Distinct8409
Distinct (%)84.1%
Missing0
Missing (%)0.0%
Memory size78.2 KiB
5a7eba68-b633-41e5-b179-fab081920fd9
 
5
ad832921-9b1d-44b3-b525-422b8623d40e
 
5
90fc6b9d-c23f-4dc6-b31f-d558a0934c82
 
4
043b7d27-16ed-4645-8fa8-74a643d3cfc1
 
4
0ee40660-8656-4ecd-9bd2-736ac22fa742
 
4
Other values (8404)
9978 

Length

Max length36
Median length36
Mean length36
Min length36

Characters and Unicode

Total characters360000
Distinct characters17
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique7005 ?
Unique (%)70.0%

Sample

1st rowcbcb03c0-3587-45cb-b910-bfc5df4367b4
2nd row93115b20-1fe1-4fec-954d-d170a38079f2
3rd row1b8a5066-9d26-4b09-ab85-2009a8c806dd
4th rowbcf09979-4060-417a-bfd2-ed8180d03714
5th rowf1f2c6b5-6a9e-42bd-ac38-03ed518cf4e8

Common Values

ValueCountFrequency (%)
5a7eba68-b633-41e5-b179-fab081920fd9 5
 
0.1%
ad832921-9b1d-44b3-b525-422b8623d40e 5
 
0.1%
90fc6b9d-c23f-4dc6-b31f-d558a0934c82 4
 
< 0.1%
043b7d27-16ed-4645-8fa8-74a643d3cfc1 4
 
< 0.1%
0ee40660-8656-4ecd-9bd2-736ac22fa742 4
 
< 0.1%
99aa1012-59d5-430a-9d5f-3d48de1b4136 4
 
< 0.1%
bc5ec321-e926-4c3e-817a-daa0b89db25e 4
 
< 0.1%
9cb9c461-30a6-4704-a0e6-7cae4e8dc14c 4
 
< 0.1%
e139f786-78e2-44e4-9b83-0fb95e058f22 4
 
< 0.1%
6ef9511a-5323-4d0e-8f3f-2e63a02fa4f3 4
 
< 0.1%
Other values (8399) 9958
99.6%

Length

2023-06-26T15:08:39.291202image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
5a7eba68-b633-41e5-b179-fab081920fd9 5
 
< 0.1%
ad832921-9b1d-44b3-b525-422b8623d40e 5
 
< 0.1%
9cb9c461-30a6-4704-a0e6-7cae4e8dc14c 4
 
< 0.1%
8125a636-f960-43fa-9db5-e77d7c55e57d 4
 
< 0.1%
6ef9511a-5323-4d0e-8f3f-2e63a02fa4f3 4
 
< 0.1%
e139f786-78e2-44e4-9b83-0fb95e058f22 4
 
< 0.1%
8e347ee8-e1e3-466e-8843-8e1283c708d7 4
 
< 0.1%
bc5ec321-e926-4c3e-817a-daa0b89db25e 4
 
< 0.1%
99aa1012-59d5-430a-9d5f-3d48de1b4136 4
 
< 0.1%
0ee40660-8656-4ecd-9bd2-736ac22fa742 4
 
< 0.1%
Other values (8399) 9958
99.6%

Most occurring characters

ValueCountFrequency (%)
- 40000
 
11.1%
4 28669
 
8.0%
9 21637
 
6.0%
8 21534
 
6.0%
b 21158
 
5.9%
a 21087
 
5.9%
6 18970
 
5.3%
1 18869
 
5.2%
2 18806
 
5.2%
e 18802
 
5.2%
Other values (7) 130468
36.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 202901
56.4%
Lowercase Letter 117099
32.5%
Dash Punctuation 40000
 
11.1%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
4 28669
14.1%
9 21637
10.7%
8 21534
10.6%
6 18970
9.3%
1 18869
9.3%
2 18806
9.3%
7 18646
9.2%
5 18612
9.2%
0 18612
9.2%
3 18546
9.1%
Lowercase Letter
ValueCountFrequency (%)
b 21158
18.1%
a 21087
18.0%
e 18802
16.1%
f 18801
16.1%
c 18633
15.9%
d 18618
15.9%
Dash Punctuation
ValueCountFrequency (%)
- 40000
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 242901
67.5%
Latin 117099
32.5%

Most frequent character per script

Common
ValueCountFrequency (%)
- 40000
16.5%
4 28669
11.8%
9 21637
8.9%
8 21534
8.9%
6 18970
7.8%
1 18869
7.8%
2 18806
7.7%
7 18646
7.7%
5 18612
7.7%
0 18612
7.7%
Latin
ValueCountFrequency (%)
b 21158
18.1%
a 21087
18.0%
e 18802
16.1%
f 18801
16.1%
c 18633
15.9%
d 18618
15.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII 360000
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
- 40000
 
11.1%
4 28669
 
8.0%
9 21637
 
6.0%
8 21534
 
6.0%
b 21158
 
5.9%
a 21087
 
5.9%
6 18970
 
5.3%
1 18869
 
5.2%
2 18806
 
5.2%
e 18802
 
5.2%
Other values (7) 130468
36.2%

Surname
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing10000
Missing (%)100.0%
Memory size78.2 KiB

Forename
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing10000
Missing (%)100.0%
Memory size78.2 KiB

Middlenames
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing10000
Missing (%)100.0%
Memory size78.2 KiB

PreferredSurname
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing10000
Missing (%)100.0%
Memory size78.2 KiB

FormerSurname
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing10000
Missing (%)100.0%
Memory size78.2 KiB

Gender
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size78.2 KiB
F
5018 
M
4982 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters10000
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowF
2nd rowF
3rd rowM
4th rowM
5th rowF

Common Values

ValueCountFrequency (%)
F 5018
50.2%
M 4982
49.8%

Length

2023-06-26T15:08:39.547756image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-06-26T15:08:39.804075image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
f 5018
50.2%
m 4982
49.8%

Most occurring characters

ValueCountFrequency (%)
F 5018
50.2%
M 4982
49.8%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 10000
100.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
F 5018
50.2%
M 4982
49.8%

Most occurring scripts

ValueCountFrequency (%)
Latin 10000
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
F 5018
50.2%
M 4982
49.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 10000
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
F 5018
50.2%
M 4982
49.8%

DoB
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing10000
Missing (%)100.0%
Memory size78.2 KiB

EnrolStatus
Categorical

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size78.2 KiB
C
9176 
M
 
819
Leaver
 
3
S
 
2

Length

Max length6
Median length1
Mean length1.0015
Min length1

Characters and Unicode

Total characters10015
Distinct characters8
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowC
2nd rowC
3rd rowC
4th rowC
5th rowC

Common Values

ValueCountFrequency (%)
C 9176
91.8%
M 819
 
8.2%
Leaver 3
 
< 0.1%
S 2
 
< 0.1%

Length

2023-06-26T15:08:40.027599image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-06-26T15:08:40.233450image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
c 9176
91.8%
m 819
 
8.2%
leaver 3
 
< 0.1%
s 2
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
C 9176
91.6%
M 819
 
8.2%
e 6
 
0.1%
L 3
 
< 0.1%
a 3
 
< 0.1%
v 3
 
< 0.1%
r 3
 
< 0.1%
S 2
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 10000
99.9%
Lowercase Letter 15
 
0.1%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
C 9176
91.8%
M 819
 
8.2%
L 3
 
< 0.1%
S 2
 
< 0.1%
Lowercase Letter
ValueCountFrequency (%)
e 6
40.0%
a 3
20.0%
v 3
20.0%
r 3
20.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 10015
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
C 9176
91.6%
M 819
 
8.2%
e 6
 
0.1%
L 3
 
< 0.1%
a 3
 
< 0.1%
v 3
 
< 0.1%
r 3
 
< 0.1%
S 2
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 10015
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
C 9176
91.6%
M 819
 
8.2%
e 6
 
0.1%
L 3
 
< 0.1%
a 3
 
< 0.1%
v 3
 
< 0.1%
r 3
 
< 0.1%
S 2
 
< 0.1%

EntryDate
Categorical

Distinct417
Distinct (%)4.2%
Missing0
Missing (%)0.0%
Memory size78.2 KiB
2019-09-04 00:00:00
1216 
2020-09-03 00:00:00
1165 
2017-09-06 00:00:00
1048 
2016-09-05 00:00:00
697 
2018-09-07 00:00:00
551 
Other values (412)
5323 

Length

Max length19
Median length19
Mean length19
Min length19

Characters and Unicode

Total characters190000
Distinct characters13
Distinct categories4 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique156 ?
Unique (%)1.6%

Sample

1st row2017-09-06 00:00:00
2nd row2020-09-03 00:00:00
3rd row2017-09-04 00:00:00
4th row2017-09-06 00:00:00
5th row2016-09-05 00:00:00

Common Values

ValueCountFrequency (%)
2019-09-04 00:00:00 1216
 
12.2%
2020-09-03 00:00:00 1165
 
11.7%
2017-09-06 00:00:00 1048
 
10.5%
2016-09-05 00:00:00 697
 
7.0%
2018-09-07 00:00:00 551
 
5.5%
2018-09-06 00:00:00 503
 
5.0%
2018-09-05 00:00:00 461
 
4.6%
2020-09-01 00:00:00 431
 
4.3%
2020-09-02 00:00:00 422
 
4.2%
2017-09-04 00:00:00 398
 
4.0%
Other values (407) 3108
31.1%

Length

2023-06-26T15:08:40.433099image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
00:00:00 10000
50.0%
2019-09-04 1216
 
6.1%
2020-09-03 1165
 
5.8%
2017-09-06 1048
 
5.2%
2016-09-05 697
 
3.5%
2018-09-07 551
 
2.8%
2018-09-06 503
 
2.5%
2018-09-05 461
 
2.3%
2020-09-01 431
 
2.2%
2020-09-02 422
 
2.1%
Other values (408) 3506
 
17.5%

Most occurring characters

ValueCountFrequency (%)
0 91612
48.2%
- 20000
 
10.5%
: 20000
 
10.5%
2 13794
 
7.3%
9 11481
 
6.0%
10000
 
5.3%
1 9765
 
5.1%
6 2809
 
1.5%
7 2584
 
1.4%
8 2353
 
1.2%
Other values (3) 5602
 
2.9%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 140000
73.7%
Dash Punctuation 20000
 
10.5%
Other Punctuation 20000
 
10.5%
Space Separator 10000
 
5.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 91612
65.4%
2 13794
 
9.9%
9 11481
 
8.2%
1 9765
 
7.0%
6 2809
 
2.0%
7 2584
 
1.8%
8 2353
 
1.7%
4 2110
 
1.5%
3 1851
 
1.3%
5 1641
 
1.2%
Dash Punctuation
ValueCountFrequency (%)
- 20000
100.0%
Other Punctuation
ValueCountFrequency (%)
: 20000
100.0%
Space Separator
ValueCountFrequency (%)
10000
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 190000
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 91612
48.2%
- 20000
 
10.5%
: 20000
 
10.5%
2 13794
 
7.3%
9 11481
 
6.0%
10000
 
5.3%
1 9765
 
5.1%
6 2809
 
1.5%
7 2584
 
1.4%
8 2353
 
1.2%
Other values (3) 5602
 
2.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII 190000
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 91612
48.2%
- 20000
 
10.5%
: 20000
 
10.5%
2 13794
 
7.3%
9 11481
 
6.0%
10000
 
5.3%
1 9765
 
5.1%
6 2809
 
1.5%
7 2584
 
1.4%
8 2353
 
1.2%
Other values (3) 5602
 
2.9%

NCyearActual
Categorical

Distinct8
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size78.2 KiB
8
2380 
10
2243 
9
2103 
11
1982 
12
1059 
Other values (3)
 
233

Length

Max length6
Median length2
Mean length1.6093
Min length1

Characters and Unicode

Total characters16093
Distinct characters12
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row8
2nd row11
3rd rowLeaver
4th row10
5th row10

Common Values

ValueCountFrequency (%)
8 2380
23.8%
10 2243
22.4%
9 2103
21.0%
11 1982
19.8%
12 1059
10.6%
Leaver 158
 
1.6%
7 56
 
0.6%
13 19
 
0.2%

Length

2023-06-26T15:08:40.692350image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-06-26T15:08:40.927400image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
8 2380
23.8%
10 2243
22.4%
9 2103
21.0%
11 1982
19.8%
12 1059
10.6%
leaver 158
 
1.6%
7 56
 
0.6%
13 19
 
0.2%

Most occurring characters

ValueCountFrequency (%)
1 7285
45.3%
8 2380
 
14.8%
0 2243
 
13.9%
9 2103
 
13.1%
2 1059
 
6.6%
e 316
 
2.0%
L 158
 
1.0%
a 158
 
1.0%
v 158
 
1.0%
r 158
 
1.0%
Other values (2) 75
 
0.5%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 15145
94.1%
Lowercase Letter 790
 
4.9%
Uppercase Letter 158
 
1.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 7285
48.1%
8 2380
 
15.7%
0 2243
 
14.8%
9 2103
 
13.9%
2 1059
 
7.0%
7 56
 
0.4%
3 19
 
0.1%
Lowercase Letter
ValueCountFrequency (%)
e 316
40.0%
a 158
20.0%
v 158
20.0%
r 158
20.0%
Uppercase Letter
ValueCountFrequency (%)
L 158
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 15145
94.1%
Latin 948
 
5.9%

Most frequent character per script

Common
ValueCountFrequency (%)
1 7285
48.1%
8 2380
 
15.7%
0 2243
 
14.8%
9 2103
 
13.9%
2 1059
 
7.0%
7 56
 
0.4%
3 19
 
0.1%
Latin
ValueCountFrequency (%)
e 316
33.3%
L 158
16.7%
a 158
16.7%
v 158
16.7%
r 158
16.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 16093
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 7285
45.3%
8 2380
 
14.8%
0 2243
 
13.9%
9 2103
 
13.1%
2 1059
 
6.6%
e 316
 
2.0%
L 158
 
1.0%
a 158
 
1.0%
v 158
 
1.0%
r 158
 
1.0%
Other values (2) 75
 
0.5%

TermlySessionsPossible
Real number (ℝ)

Distinct71
Distinct (%)0.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean107.9804
Minimum42
Maximum124
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size78.2 KiB
2023-06-26T15:08:41.172627image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum42
5-th percentile84
Q1101
median111
Q3118
95-th percentile123
Maximum124
Range82
Interquartile range (IQR)17

Descriptive statistics

Standard deviation12.076183
Coefficient of variation (CV)0.11183681
Kurtosis0.69155497
Mean107.9804
Median Absolute Deviation (MAD)8
Skewness-0.95706672
Sum1079804
Variance145.8342
MonotonicityNot monotonic
2023-06-26T15:08:41.435536image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
120 412
 
4.1%
121 405
 
4.0%
122 395
 
4.0%
123 387
 
3.9%
116 381
 
3.8%
119 365
 
3.6%
118 361
 
3.6%
114 360
 
3.6%
113 359
 
3.6%
112 354
 
3.5%
Other values (61) 6221
62.2%
ValueCountFrequency (%)
42 1
 
< 0.1%
51 1
 
< 0.1%
53 1
 
< 0.1%
55 1
 
< 0.1%
56 1
 
< 0.1%
57 2
< 0.1%
58 1
 
< 0.1%
61 2
< 0.1%
62 3
< 0.1%
63 2
< 0.1%
ValueCountFrequency (%)
124 215
2.1%
123 387
3.9%
122 395
4.0%
121 405
4.0%
120 412
4.1%
119 365
3.6%
118 361
3.6%
117 337
3.4%
116 381
3.8%
115 353
3.5%

/
Real number (ℝ)

Distinct62
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean46.9306
Minimum1
Maximum62
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size78.2 KiB
2023-06-26T15:08:41.723933image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile25
Q140
median49
Q356
95-th percentile61
Maximum62
Range61
Interquartile range (IQR)16

Descriptive statistics

Standard deviation11.376165
Coefficient of variation (CV)0.24240399
Kurtosis0.50087823
Mean46.9306
Median Absolute Deviation (MAD)8
Skewness-0.92846806
Sum469306
Variance129.41713
MonotonicityNot monotonic
2023-06-26T15:08:42.296570image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
57 433
 
4.3%
58 429
 
4.3%
60 426
 
4.3%
56 418
 
4.2%
61 413
 
4.1%
54 406
 
4.1%
59 406
 
4.1%
55 378
 
3.8%
53 363
 
3.6%
49 348
 
3.5%
Other values (52) 5980
59.8%
ValueCountFrequency (%)
1 1
 
< 0.1%
2 2
 
< 0.1%
3 2
 
< 0.1%
4 3
 
< 0.1%
5 5
 
0.1%
6 8
0.1%
7 6
0.1%
8 9
0.1%
9 13
0.1%
10 8
0.1%
ValueCountFrequency (%)
62 221
2.2%
61 413
4.1%
60 426
4.3%
59 406
4.1%
58 429
4.3%
57 433
4.3%
56 418
4.2%
55 378
3.8%
54 406
4.1%
53 363
3.6%

\
Real number (ℝ)

Distinct61
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean47.2563
Minimum2
Maximum62
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size78.2 KiB
2023-06-26T15:08:42.565743image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum2
5-th percentile26
Q141
median49
Q356
95-th percentile61
Maximum62
Range60
Interquartile range (IQR)15

Descriptive statistics

Standard deviation11.114106
Coefficient of variation (CV)0.23518783
Kurtosis0.57453129
Mean47.2563
Median Absolute Deviation (MAD)8
Skewness-0.94013959
Sum472563
Variance123.52336
MonotonicityNot monotonic
2023-06-26T15:08:42.812927image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
59 440
 
4.4%
58 434
 
4.3%
60 433
 
4.3%
57 433
 
4.3%
56 430
 
4.3%
61 425
 
4.2%
55 404
 
4.0%
54 369
 
3.7%
53 369
 
3.7%
50 358
 
3.6%
Other values (51) 5905
59.1%
ValueCountFrequency (%)
2 1
 
< 0.1%
3 2
 
< 0.1%
4 11
0.1%
5 3
 
< 0.1%
6 3
 
< 0.1%
7 6
0.1%
8 7
0.1%
9 6
0.1%
10 8
0.1%
11 10
0.1%
ValueCountFrequency (%)
62 211
2.1%
61 425
4.2%
60 433
4.3%
59 440
4.4%
58 434
4.3%
57 433
4.3%
56 430
4.3%
55 404
4.0%
54 369
3.7%
53 369
3.7%

B
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing10000
Missing (%)100.0%
Memory size78.2 KiB

J
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing10000
Missing (%)100.0%
Memory size78.2 KiB

L
Real number (ℝ)

Distinct20
Distinct (%)0.5%
Missing5599
Missing (%)56.0%
Infinite0
Infinite (%)0.0%
Mean4.4880709
Minimum1
Maximum20
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size78.2 KiB
2023-06-26T15:08:43.058606image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q12
median4
Q36
95-th percentile10
Maximum20
Range19
Interquartile range (IQR)4

Descriptive statistics

Standard deviation2.9423871
Coefficient of variation (CV)0.65560174
Kurtosis1.4820271
Mean4.4880709
Median Absolute Deviation (MAD)2
Skewness1.1726591
Sum19752
Variance8.6576418
MonotonicityNot monotonic
2023-06-26T15:08:43.255014image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=20)
ValueCountFrequency (%)
2 829
 
8.3%
3 677
 
6.8%
4 580
 
5.8%
1 506
 
5.1%
5 482
 
4.8%
6 399
 
4.0%
7 269
 
2.7%
8 194
 
1.9%
9 159
 
1.6%
10 102
 
1.0%
Other values (10) 204
 
2.0%
(Missing) 5599
56.0%
ValueCountFrequency (%)
1 506
5.1%
2 829
8.3%
3 677
6.8%
4 580
5.8%
5 482
4.8%
6 399
4.0%
7 269
 
2.7%
8 194
 
1.9%
9 159
 
1.6%
10 102
 
1.0%
ValueCountFrequency (%)
20 2
 
< 0.1%
19 1
 
< 0.1%
18 1
 
< 0.1%
17 5
 
0.1%
16 9
 
0.1%
15 4
 
< 0.1%
14 19
 
0.2%
13 35
0.4%
12 52
0.5%
11 76
0.8%

P
Categorical

HIGH CORRELATION  MISSING 

Distinct4
Distinct (%)0.6%
Missing9331
Missing (%)93.3%
Memory size78.2 KiB
2.0
333 
1.0
257 
3.0
74 
4.0
 
5

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters2007
Distinct characters6
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2.0
2nd row1.0
3rd row1.0
4th row2.0
5th row2.0

Common Values

ValueCountFrequency (%)
2.0 333
 
3.3%
1.0 257
 
2.6%
3.0 74
 
0.7%
4.0 5
 
0.1%
(Missing) 9331
93.3%

Length

2023-06-26T15:08:43.466515image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-06-26T15:08:43.680301image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
2.0 333
49.8%
1.0 257
38.4%
3.0 74
 
11.1%
4.0 5
 
0.7%

Most occurring characters

ValueCountFrequency (%)
. 669
33.3%
0 669
33.3%
2 333
16.6%
1 257
 
12.8%
3 74
 
3.7%
4 5
 
0.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1338
66.7%
Other Punctuation 669
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 669
50.0%
2 333
24.9%
1 257
 
19.2%
3 74
 
5.5%
4 5
 
0.4%
Other Punctuation
ValueCountFrequency (%)
. 669
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 2007
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
. 669
33.3%
0 669
33.3%
2 333
16.6%
1 257
 
12.8%
3 74
 
3.7%
4 5
 
0.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 2007
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
. 669
33.3%
0 669
33.3%
2 333
16.6%
1 257
 
12.8%
3 74
 
3.7%
4 5
 
0.2%

V
Categorical

HIGH CORRELATION  MISSING 

Distinct5
Distinct (%)0.2%
Missing7227
Missing (%)72.3%
Memory size78.2 KiB
2.0
1182 
3.0
810 
1.0
483 
4.0
260 
5.0
 
38

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters8319
Distinct characters7
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1.0
2nd row3.0
3rd row1.0
4th row3.0
5th row4.0

Common Values

ValueCountFrequency (%)
2.0 1182
 
11.8%
3.0 810
 
8.1%
1.0 483
 
4.8%
4.0 260
 
2.6%
5.0 38
 
0.4%
(Missing) 7227
72.3%

Length

2023-06-26T15:08:43.876605image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-06-26T15:08:44.090513image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
2.0 1182
42.6%
3.0 810
29.2%
1.0 483
17.4%
4.0 260
 
9.4%
5.0 38
 
1.4%

Most occurring characters

ValueCountFrequency (%)
. 2773
33.3%
0 2773
33.3%
2 1182
14.2%
3 810
 
9.7%
1 483
 
5.8%
4 260
 
3.1%
5 38
 
0.5%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 5546
66.7%
Other Punctuation 2773
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 2773
50.0%
2 1182
21.3%
3 810
 
14.6%
1 483
 
8.7%
4 260
 
4.7%
5 38
 
0.7%
Other Punctuation
ValueCountFrequency (%)
. 2773
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 8319
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
. 2773
33.3%
0 2773
33.3%
2 1182
14.2%
3 810
 
9.7%
1 483
 
5.8%
4 260
 
3.1%
5 38
 
0.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 8319
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
. 2773
33.3%
0 2773
33.3%
2 1182
14.2%
3 810
 
9.7%
1 483
 
5.8%
4 260
 
3.1%
5 38
 
0.5%

W
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing10000
Missing (%)100.0%
Memory size78.2 KiB

C
Real number (ℝ)

Distinct56
Distinct (%)1.4%
Missing5884
Missing (%)58.8%
Infinite0
Infinite (%)0.0%
Mean16.319728
Minimum1
Maximum61
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size78.2 KiB
2023-06-26T15:08:44.347107image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile2
Q18
median15
Q323
95-th percentile35
Maximum61
Range60
Interquartile range (IQR)15

Descriptive statistics

Standard deviation10.270169
Coefficient of variation (CV)0.62931004
Kurtosis0.072292753
Mean16.319728
Median Absolute Deviation (MAD)7
Skewness0.68346689
Sum67172
Variance105.47636
MonotonicityNot monotonic
2023-06-26T15:08:44.569451image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
10 166
 
1.7%
12 160
 
1.6%
5 160
 
1.6%
7 159
 
1.6%
14 157
 
1.6%
8 156
 
1.6%
4 153
 
1.5%
13 146
 
1.5%
2 145
 
1.5%
6 143
 
1.4%
Other values (46) 2571
25.7%
(Missing) 5884
58.8%
ValueCountFrequency (%)
1 69
0.7%
2 145
1.5%
3 124
1.2%
4 153
1.5%
5 160
1.6%
6 143
1.4%
7 159
1.6%
8 156
1.6%
9 126
1.3%
10 166
1.7%
ValueCountFrequency (%)
61 1
 
< 0.1%
60 1
 
< 0.1%
57 1
 
< 0.1%
54 1
 
< 0.1%
53 3
< 0.1%
52 3
< 0.1%
51 1
 
< 0.1%
49 5
0.1%
48 3
< 0.1%
47 2
 
< 0.1%

E
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing10000
Missing (%)100.0%
Memory size78.2 KiB

H
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing10000
Missing (%)100.0%
Memory size78.2 KiB

I
Real number (ℝ)

Distinct31
Distinct (%)0.6%
Missing4369
Missing (%)43.7%
Infinite0
Infinite (%)0.0%
Mean7.1191618
Minimum1
Maximum32
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size78.2 KiB
2023-06-26T15:08:44.784234image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q13
median6
Q310
95-th percentile17
Maximum32
Range31
Interquartile range (IQR)7

Descriptive statistics

Standard deviation4.9274135
Coefficient of variation (CV)0.69213394
Kurtosis0.85390586
Mean7.1191618
Median Absolute Deviation (MAD)3
Skewness1.021669
Sum40088
Variance24.279404
MonotonicityNot monotonic
2023-06-26T15:08:44.988336image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=31)
ValueCountFrequency (%)
2 637
 
6.4%
3 601
 
6.0%
4 521
 
5.2%
5 481
 
4.8%
6 425
 
4.2%
1 369
 
3.7%
8 364
 
3.6%
7 361
 
3.6%
9 305
 
3.0%
10 292
 
2.9%
Other values (21) 1275
 
12.8%
(Missing) 4369
43.7%
ValueCountFrequency (%)
1 369
3.7%
2 637
6.4%
3 601
6.0%
4 521
5.2%
5 481
4.8%
6 425
4.2%
7 361
3.6%
8 364
3.6%
9 305
3.0%
10 292
2.9%
ValueCountFrequency (%)
32 1
 
< 0.1%
30 1
 
< 0.1%
29 2
 
< 0.1%
28 3
 
< 0.1%
27 4
 
< 0.1%
26 2
 
< 0.1%
25 8
0.1%
24 1
 
< 0.1%
23 19
0.2%
22 18
0.2%

M
Categorical

Distinct5
Distinct (%)0.2%
Missing6954
Missing (%)69.5%
Memory size78.2 KiB
1.0
1397 
2.0
1266 
3.0
310 
4.0
 
63
5.0
 
10

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters9138
Distinct characters7
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2.0
2nd row3.0
3rd row1.0
4th row2.0
5th row2.0

Common Values

ValueCountFrequency (%)
1.0 1397
 
14.0%
2.0 1266
 
12.7%
3.0 310
 
3.1%
4.0 63
 
0.6%
5.0 10
 
0.1%
(Missing) 6954
69.5%

Length

2023-06-26T15:08:45.157115image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-06-26T15:08:45.351709image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
1.0 1397
45.9%
2.0 1266
41.6%
3.0 310
 
10.2%
4.0 63
 
2.1%
5.0 10
 
0.3%

Most occurring characters

ValueCountFrequency (%)
. 3046
33.3%
0 3046
33.3%
1 1397
15.3%
2 1266
13.9%
3 310
 
3.4%
4 63
 
0.7%
5 10
 
0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 6092
66.7%
Other Punctuation 3046
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 3046
50.0%
1 1397
22.9%
2 1266
20.8%
3 310
 
5.1%
4 63
 
1.0%
5 10
 
0.2%
Other Punctuation
ValueCountFrequency (%)
. 3046
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 9138
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
. 3046
33.3%
0 3046
33.3%
1 1397
15.3%
2 1266
13.9%
3 310
 
3.4%
4 63
 
0.7%
5 10
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 9138
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
. 3046
33.3%
0 3046
33.3%
1 1397
15.3%
2 1266
13.9%
3 310
 
3.4%
4 63
 
0.7%
5 10
 
0.1%

R
Categorical

HIGH CORRELATION  MISSING 

Distinct4
Distinct (%)0.3%
Missing8544
Missing (%)85.4%
Memory size78.2 KiB
2.0
810 
3.0
640 
1.0
 
5
4.0
 
1

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters4368
Distinct characters6
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)0.1%

Sample

1st row3.0
2nd row3.0
3rd row2.0
4th row3.0
5th row3.0

Common Values

ValueCountFrequency (%)
2.0 810
 
8.1%
3.0 640
 
6.4%
1.0 5
 
0.1%
4.0 1
 
< 0.1%
(Missing) 8544
85.4%

Length

2023-06-26T15:08:45.584140image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-06-26T15:08:45.758503image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
2.0 810
55.6%
3.0 640
44.0%
1.0 5
 
0.3%
4.0 1
 
0.1%

Most occurring characters

ValueCountFrequency (%)
. 1456
33.3%
0 1456
33.3%
2 810
18.5%
3 640
14.7%
1 5
 
0.1%
4 1
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 2912
66.7%
Other Punctuation 1456
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 1456
50.0%
2 810
27.8%
3 640
22.0%
1 5
 
0.2%
4 1
 
< 0.1%
Other Punctuation
ValueCountFrequency (%)
. 1456
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 4368
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
. 1456
33.3%
0 1456
33.3%
2 810
18.5%
3 640
14.7%
1 5
 
0.1%
4 1
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 4368
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
. 1456
33.3%
0 1456
33.3%
2 810
18.5%
3 640
14.7%
1 5
 
0.1%
4 1
 
< 0.1%

S
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct9
Distinct (%)8.7%
Missing9897
Missing (%)99.0%
Infinite0
Infinite (%)0.0%
Mean11.873786
Minimum7
Maximum15
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size78.2 KiB
2023-06-26T15:08:45.981719image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum7
5-th percentile10
Q111
median12
Q313
95-th percentile14
Maximum15
Range8
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.5446277
Coefficient of variation (CV)0.13008721
Kurtosis0.26893302
Mean11.873786
Median Absolute Deviation (MAD)1
Skewness-0.20794115
Sum1223
Variance2.3858747
MonotonicityNot monotonic
2023-06-26T15:08:46.168564image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=9)
ValueCountFrequency (%)
12 31
 
0.3%
11 22
 
0.2%
13 15
 
0.1%
10 13
 
0.1%
14 13
 
0.1%
15 4
 
< 0.1%
9 3
 
< 0.1%
8 1
 
< 0.1%
7 1
 
< 0.1%
(Missing) 9897
99.0%
ValueCountFrequency (%)
7 1
 
< 0.1%
8 1
 
< 0.1%
9 3
 
< 0.1%
10 13
0.1%
11 22
0.2%
12 31
0.3%
13 15
0.1%
14 13
0.1%
15 4
 
< 0.1%
ValueCountFrequency (%)
15 4
 
< 0.1%
14 13
0.1%
13 15
0.1%
12 31
0.3%
11 22
0.2%
10 13
0.1%
9 3
 
< 0.1%
8 1
 
< 0.1%
7 1
 
< 0.1%

T
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing10000
Missing (%)100.0%
Memory size78.2 KiB

G
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing10000
Missing (%)100.0%
Memory size78.2 KiB

N
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing10000
Missing (%)100.0%
Memory size78.2 KiB

O
Real number (ℝ)

Distinct23
Distinct (%)1.0%
Missing7726
Missing (%)77.3%
Infinite0
Infinite (%)0.0%
Mean5.2005277
Minimum1
Maximum24
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size78.2 KiB
2023-06-26T15:08:46.355682image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q12
median4
Q37
95-th percentile13
Maximum24
Range23
Interquartile range (IQR)5

Descriptive statistics

Standard deviation3.70518
Coefficient of variation (CV)0.71246231
Kurtosis1.4804484
Mean5.2005277
Median Absolute Deviation (MAD)2
Skewness1.2124081
Sum11826
Variance13.728359
MonotonicityNot monotonic
2023-06-26T15:08:46.510402image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=23)
ValueCountFrequency (%)
2 376
 
3.8%
3 313
 
3.1%
1 260
 
2.6%
4 246
 
2.5%
5 223
 
2.2%
6 186
 
1.9%
7 146
 
1.5%
8 124
 
1.2%
9 97
 
1.0%
10 79
 
0.8%
Other values (13) 224
 
2.2%
(Missing) 7726
77.3%
ValueCountFrequency (%)
1 260
2.6%
2 376
3.8%
3 313
3.1%
4 246
2.5%
5 223
2.2%
6 186
1.9%
7 146
 
1.5%
8 124
 
1.2%
9 97
 
1.0%
10 79
 
0.8%
ValueCountFrequency (%)
24 1
 
< 0.1%
22 1
 
< 0.1%
21 3
 
< 0.1%
20 4
 
< 0.1%
19 2
 
< 0.1%
18 4
 
< 0.1%
17 9
 
0.1%
16 11
0.1%
15 16
0.2%
14 26
0.3%

U
Categorical

HIGH CORRELATION  MISSING 

Distinct2
Distinct (%)2.3%
Missing9913
Missing (%)99.1%
Memory size78.2 KiB
1.0
48 
2.0
39 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters261
Distinct characters4
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2.0
2nd row1.0
3rd row2.0
4th row2.0
5th row1.0

Common Values

ValueCountFrequency (%)
1.0 48
 
0.5%
2.0 39
 
0.4%
(Missing) 9913
99.1%

Length

2023-06-26T15:08:46.754415image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-06-26T15:08:46.972540image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
1.0 48
55.2%
2.0 39
44.8%

Most occurring characters

ValueCountFrequency (%)
. 87
33.3%
0 87
33.3%
1 48
18.4%
2 39
14.9%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 174
66.7%
Other Punctuation 87
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 87
50.0%
1 48
27.6%
2 39
22.4%
Other Punctuation
ValueCountFrequency (%)
. 87
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 261
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
. 87
33.3%
0 87
33.3%
1 48
18.4%
2 39
14.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII 261
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
. 87
33.3%
0 87
33.3%
1 48
18.4%
2 39
14.9%

D
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing10000
Missing (%)100.0%
Memory size78.2 KiB

X
Real number (ℝ)

Distinct42
Distinct (%)0.9%
Missing5423
Missing (%)54.2%
Infinite0
Infinite (%)0.0%
Mean12.904086
Minimum1
Maximum46
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size78.2 KiB
2023-06-26T15:08:47.221477image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile2
Q17
median12
Q318
95-th percentile26
Maximum46
Range45
Interquartile range (IQR)11

Descriptive statistics

Standard deviation7.163166
Coefficient of variation (CV)0.55510837
Kurtosis-0.12888563
Mean12.904086
Median Absolute Deviation (MAD)5
Skewness0.50217857
Sum59062
Variance51.310947
MonotonicityNot monotonic
2023-06-26T15:08:47.460668image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=42)
ValueCountFrequency (%)
11 258
 
2.6%
13 246
 
2.5%
9 241
 
2.4%
12 230
 
2.3%
8 222
 
2.2%
16 219
 
2.2%
14 216
 
2.2%
7 213
 
2.1%
10 211
 
2.1%
15 207
 
2.1%
Other values (32) 2314
23.1%
(Missing) 5423
54.2%
ValueCountFrequency (%)
1 75
 
0.8%
2 165
1.7%
3 168
1.7%
4 176
1.8%
5 195
1.9%
6 186
1.9%
7 213
2.1%
8 222
2.2%
9 241
2.4%
10 211
2.1%
ValueCountFrequency (%)
46 1
 
< 0.1%
41 1
 
< 0.1%
40 1
 
< 0.1%
39 1
 
< 0.1%
38 1
 
< 0.1%
37 3
 
< 0.1%
36 3
 
< 0.1%
35 3
 
< 0.1%
34 4
 
< 0.1%
33 12
0.1%

Y
Categorical

HIGH CORRELATION  MISSING 

Distinct3
Distinct (%)0.6%
Missing9505
Missing (%)95.0%
Memory size78.2 KiB
2.0
345 
1.0
100 
3.0
50 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters1485
Distinct characters5
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2.0
2nd row2.0
3rd row1.0
4th row2.0
5th row2.0

Common Values

ValueCountFrequency (%)
2.0 345
 
3.5%
1.0 100
 
1.0%
3.0 50
 
0.5%
(Missing) 9505
95.0%

Length

2023-06-26T15:08:47.706363image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-06-26T15:08:47.907462image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
2.0 345
69.7%
1.0 100
 
20.2%
3.0 50
 
10.1%

Most occurring characters

ValueCountFrequency (%)
. 495
33.3%
0 495
33.3%
2 345
23.2%
1 100
 
6.7%
3 50
 
3.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 990
66.7%
Other Punctuation 495
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 495
50.0%
2 345
34.8%
1 100
 
10.1%
3 50
 
5.1%
Other Punctuation
ValueCountFrequency (%)
. 495
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 1485
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
. 495
33.3%
0 495
33.3%
2 345
23.2%
1 100
 
6.7%
3 50
 
3.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1485
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
. 495
33.3%
0 495
33.3%
2 345
23.2%
1 100
 
6.7%
3 50
 
3.4%

Interactions

2023-06-26T15:08:34.047757image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-26T15:08:15.015238image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-26T15:08:17.192533image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-26T15:08:19.211538image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-26T15:08:21.115449image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-26T15:08:23.295579image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-26T15:08:25.585797image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-26T15:08:27.645548image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-26T15:08:29.761032image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-26T15:08:31.622450image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-26T15:08:34.284653image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-26T15:08:15.272833image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-26T15:08:17.480008image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-26T15:08:19.430363image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-26T15:08:21.283392image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-26T15:08:23.498732image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-26T15:08:25.781534image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-26T15:08:27.896318image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-26T15:08:29.925882image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-26T15:08:31.793596image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-26T15:08:34.583564image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-26T15:08:15.506717image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-26T15:08:17.667199image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-26T15:08:19.613924image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-26T15:08:21.469749image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-26T15:08:23.728511image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-26T15:08:26.013490image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-26T15:08:28.149314image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-26T15:08:30.148435image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-26T15:08:31.964845image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-26T15:08:34.848051image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-26T15:08:15.693925image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-26T15:08:17.838299image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-26T15:08:19.832388image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-26T15:08:21.676390image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-26T15:08:23.966980image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-26T15:08:26.278329image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-26T15:08:28.368652image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-26T15:08:30.360363image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-26T15:08:32.158494image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-26T15:08:35.135414image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-26T15:08:15.880271image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-26T15:08:18.026405image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-26T15:08:19.998539image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-26T15:08:21.863451image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-26T15:08:24.214663image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-26T15:08:26.440502image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-26T15:08:28.608610image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-26T15:08:30.498226image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-26T15:08:32.340152image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-26T15:08:35.406341image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-26T15:08:16.101260image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-26T15:08:18.232194image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-26T15:08:20.166514image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-26T15:08:22.052329image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-26T15:08:24.446507image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-26T15:08:26.629866image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-26T15:08:28.784572image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-26T15:08:30.658098image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-26T15:08:32.534600image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-26T15:08:35.666669image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-26T15:08:16.345861image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-26T15:08:18.452916image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-26T15:08:20.394994image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-26T15:08:22.276203image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-26T15:08:24.677311image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-26T15:08:26.831391image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-26T15:08:29.000293image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-26T15:08:30.853517image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-26T15:08:32.734446image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-26T15:08:35.913047image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-26T15:08:16.546281image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-26T15:08:18.608599image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-26T15:08:20.579815image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-26T15:08:22.706868image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-26T15:08:24.917285image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-26T15:08:27.032554image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-26T15:08:29.214560image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-26T15:08:31.022710image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-26T15:08:32.939717image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-26T15:08:36.163290image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-26T15:08:16.750544image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-26T15:08:18.833553image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-26T15:08:20.768325image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-26T15:08:22.902488image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-26T15:08:25.125298image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-26T15:08:27.256091image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-26T15:08:29.407845image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-26T15:08:31.218517image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-26T15:08:33.240631image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-26T15:08:36.402730image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-26T15:08:16.942697image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-26T15:08:19.026127image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-26T15:08:20.916976image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-26T15:08:23.099339image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-26T15:08:25.352962image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-26T15:08:27.464625image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-26T15:08:29.590522image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-26T15:08:31.405525image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-26T15:08:33.490268image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Correlations

2023-06-26T15:08:48.122381image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
EstabTermlySessionsPossible/\LCISOXGenderEnrolStatusNCyearActualPVMRUY
Estab1.0000.0990.0990.052-0.007-0.017-0.025-0.002-0.005-0.2900.0450.0400.0930.0000.0000.0550.0000.0390.000
TermlySessionsPossible0.0991.0000.5680.6400.014-0.1950.0260.027-0.003-0.4100.0000.0080.0940.0570.0120.0000.0000.3080.000
/0.0990.5681.0000.885-0.215-0.415-0.178-0.052-0.094-0.3980.0150.0130.1210.0470.0380.0670.0410.0000.072
\0.0520.6400.8851.000-0.067-0.466-0.134-0.094-0.076-0.4050.0000.0190.1440.0200.0310.0370.0000.1770.134
L-0.0070.014-0.215-0.0671.000-0.0330.051-0.0950.0360.0040.0250.0000.0000.0000.0000.0000.0000.2660.025
C-0.017-0.195-0.415-0.466-0.0331.000-0.035-0.218-0.0890.1950.0000.0000.1120.0000.0000.0000.0000.3460.166
I-0.0250.026-0.178-0.1340.051-0.0351.000-0.156-0.017-0.0040.0000.0130.0000.0410.0000.0900.0000.0000.000
S-0.0020.027-0.052-0.094-0.095-0.218-0.1561.000-0.0100.0410.0660.1870.1540.6320.3190.0000.563NaN1.000
O-0.005-0.003-0.094-0.0760.036-0.089-0.017-0.0101.000-0.0080.0000.0000.0000.0000.0910.0560.1180.0000.000
X-0.290-0.410-0.398-0.4050.0040.195-0.0040.041-0.0081.0000.0000.0000.0500.0000.0200.0570.0000.2590.101
Gender0.0450.0000.0150.0000.0250.0000.0000.0660.0000.0001.0000.0050.0160.0000.0000.0210.0000.0000.029
EnrolStatus0.0400.0080.0130.0190.0000.0000.0130.1870.0000.0000.0051.0000.0000.0000.0000.0000.0000.0000.000
NCyearActual0.0930.0940.1210.1440.0000.1120.0000.1540.0000.0500.0160.0001.0000.0430.0470.0000.0000.2460.000
P0.0000.0570.0470.0200.0000.0000.0410.6320.0000.0000.0000.0000.0431.0000.1070.1090.0461.0000.000
V0.0000.0120.0380.0310.0000.0000.0000.3190.0910.0200.0000.0000.0470.1071.0000.0000.0000.6380.137
M0.0550.0000.0670.0370.0000.0000.0900.0000.0560.0570.0210.0000.0000.1090.0001.0000.0000.0000.000
R0.0000.0000.0410.0000.0000.0000.0000.5630.1180.0000.0000.0000.0000.0460.0000.0001.0000.0000.000
U0.0390.3080.0000.1770.2660.3460.000NaN0.0000.2590.0000.0000.2461.0000.6380.0000.0001.0000.000
Y0.0000.0000.0720.1340.0250.1660.0001.0000.0000.1010.0290.0000.0000.0000.1370.0000.0000.0001.000

Missing values

2023-06-26T15:08:36.775426image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
A simple visualization of nullity by column.
2023-06-26T15:08:37.474135image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2023-06-26T15:08:38.131914image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

EstabUPNSurnameForenameMiddlenamesPreferredSurnameFormerSurnameGenderDoBEnrolStatusEntryDateNCyearActualTermlySessionsPossible/\BJLPVWCEHIMRSTGNOUDXY
0161990cbcb03c0-3587-45cb-b910-bfc5df4367b4NaNNaNNaNNaNNaNFNaNC2017-09-06 00:00:0081152736NaNNaNNaNNaNNaNNaN43.0NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
122603293115b20-1fe1-4fec-954d-d170a38079f2NaNNaNNaNNaNNaNFNaNC2020-09-03 00:00:00111216260NaNNaN1.0NaN1.0NaNNaNNaNNaN1.0NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
21990181b8a5066-9d26-4b09-ab85-2009a8c806ddNaNNaNNaNNaNNaNMNaNC2017-09-04 00:00:00Leaver1003623NaNNaNNaNNaNNaNNaN25.0NaNNaNNaNNaN3.0NaNNaNNaNNaNNaNNaNNaN21.0NaN
3113961bcf09979-4060-417a-bfd2-ed8180d03714NaNNaNNaNNaNNaNMNaNC2017-09-06 00:00:00101113940NaNNaN6.0NaNNaNNaN35.0NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN20.0NaN
4189738f1f2c6b5-6a9e-42bd-ac38-03ed518cf4e8NaNNaNNaNNaNNaNFNaNC2016-09-05 00:00:0010985145NaNNaN2.0NaN3.0NaN7.0NaNNaNNaNNaNNaNNaNNaNNaNNaN3.0NaNNaN3.0NaN
5206068fc2e9f6a-3408-4c0b-8d9e-363595a3b6d5NaNNaNNaNNaNNaNMNaNC2017-09-06 00:00:0011855347NaNNaN4.0NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
6146033e29a1455-88fd-45c5-be96-ca574fc4bd71NaNNaNNaNNaNNaNFNaNC2020-09-01 00:00:0011916051NaNNaNNaNNaN1.0NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
71669651dcc381a-8034-4dfa-94b6-d603995ff5feNaNNaNNaNNaNNaNFNaNC2018-09-07 00:00:0091235657NaNNaN2.0NaNNaNNaNNaNNaNNaN2.0NaN3.0NaNNaNNaNNaNNaNNaNNaNNaN2.0
8168050402e9cbd-4a98-4843-943f-3011cd8d0fe8NaNNaNNaNNaNNaNMNaNM2021-10-04 00:00:00111194247NaNNaN3.02.0NaNNaNNaNNaNNaN6.02.0NaNNaNNaNNaNNaN1.0NaNNaN15.0NaN
9189983a7979765-65d1-4688-9fb3-d077ba3eb21bNaNNaNNaNNaNNaNMNaNC2016-09-05 00:00:0091075754NaNNaNNaNNaNNaNNaN20.0NaNNaN4.0NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
EstabUPNSurnameForenameMiddlenamesPreferredSurnameFormerSurnameGenderDoBEnrolStatusEntryDateNCyearActualTermlySessionsPossible/\BJLPVWCEHIMRSTGNOUDXY
9990187435f32d7037-5836-48bc-80cf-caf6dc4b01e4NaNNaNNaNNaNNaNFNaNC2020-09-03 00:00:0010925147NaNNaN2.0NaNNaNNaNNaNNaNNaN1.0NaNNaNNaNNaNNaNNaNNaNNaNNaN9.0NaN
9991199610da0b2f15-3b02-44c6-afbb-5cdcd5adb4f7NaNNaNNaNNaNNaNFNaNC2016-09-05 00:00:0081216162NaNNaN4.0NaNNaNNaNNaNNaNNaN8.0NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
99922616073ebb18b2-7dde-4410-935f-a62ace05b12cNaNNaNNaNNaNNaNMNaNC2020-09-01 00:00:0012942625NaNNaNNaNNaNNaNNaN30.0NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN6.02.0
9993129257fcad0ebf-ec0a-404a-abbf-8c3edff2da58NaNNaNNaNNaNNaNFNaNC2019-09-04 00:00:00101215558NaNNaN9.0NaNNaNNaNNaNNaNNaN6.0NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
999418983020334a99-0537-4165-b0a1-3563dcf30770NaNNaNNaNNaNNaNFNaNC2020-09-03 00:00:00111174450NaNNaN2.0NaNNaNNaNNaNNaNNaNNaN1.0NaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
9995148283286db8b2-4bc5-45e2-8011-141384d476b0NaNNaNNaNNaNNaNFNaNC2020-09-02 00:00:00111003434NaNNaNNaNNaNNaNNaN34.0NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN23.0NaN
9996257041e6b432fb-b3f1-460c-8e4e-0542124dd1e6NaNNaNNaNNaNNaNFNaNM2019-09-03 00:00:0091105955NaNNaNNaNNaN3.0NaNNaNNaNNaN2.0NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
9997215808b64551a4-e9f0-4d36-aa74-ab173f588b43NaNNaNNaNNaNNaNMNaNC2017-09-06 00:00:0012945147NaNNaN1.0NaNNaNNaN11.0NaNNaN2.0NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
9998126196b3635a9e-0784-4bad-9271-7289ddd8ffefNaNNaNNaNNaNNaNFNaNM2017-09-04 00:00:0011873538NaNNaNNaNNaNNaNNaNNaNNaNNaN5.0NaNNaNNaNNaNNaNNaNNaNNaNNaN23.0NaN
9999194365399ee14d-0d7b-46dd-8d71-325aa49bd26eNaNNaNNaNNaNNaNMNaNC2020-09-02 00:00:0012752420NaNNaNNaNNaNNaNNaN37.0NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN35.0NaN